#importing necerssary files

import cv2

import numpy as np

#!pip install bokeh

from bokeh.plotting import figure

from bokeh.io import output_notebook, show, push_notebook

import time

output_notebook()

​

cap2 = cv2.VideoCapture('./samples/vtest.avi')

ret, frame = cap2.read()

frame=cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA) # because Bokeh expects a RGBA image

frame=cv2.flip(frame, -1) # because Bokeh flips vertically

width=int(frame.shape[1])

height=int(frame.shape[0])

p = figure(x_range=(0,width), y_range=(0,height), output_backend="webgl", width=width, height=height)

myImage = p.image_rgba(image=[frame], x=0, y=0, dw=width, dh=height)

show(p, notebook_handle=True)

while True:

    ret2, frame = cap2.read()

    if not ret2:

        break

    frame=cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)

    frame=cv2.flip(frame, -1)

    myImage.data_source.data['image']=[frame]

    push_notebook()

    time.sleep(0.01)

cap2.release()

cap = cv2.VideoCapture('./samples/vtest.avi')

​

video_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)

video_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)

video_width, video_height = int(video_width), int(video_height)

​

#encode

fourcc = cv2.VideoWriter_fourcc(*'XVID')

out = cv2.VideoWriter('motion_detection_result.avi', fourcc, 30.0, (video_width,video_height))

​

ret, frame1 = cap.read()

ret, frame2 = cap.read()

​

#video conversion

while cap.isOpened():

    diff = cv2.absdiff(frame1, frame2)

    gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)

    blur = cv2.GaussianBlur(gray, (5,5), 0)

    _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)

    dilated = cv2.dilate(thresh, None, iterations=3)

    contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    #placing rectangles around moving objects

    for contour in contours:

        (x, y, w, h) = cv2.boundingRect(contour)

        if cv2.contourArea(contour) < 500:

            continue

        cv2.rectangle(frame1, (x,y), (x+w, y+h), (0,255,0), 2)

​

    #new video feed

    cv2.imshow('feed', frame1)

    out.write(frame1)    #saving the video to an avi file

    frame1 = frame2

    ret2, frame2 = cap.read()

    if cv2.waitKey(1) == ord('q') or not ret2:

        break

cap.release()

cv2.destroyAllWindows()

output_notebook()

​

cap2 = cv2.VideoCapture('motion_detection_result.avi')

ret, frame = cap2.read()

frame=cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA) # because Bokeh expects a RGBA image

frame=cv2.flip(frame, -1) # because Bokeh flips vertically

width=int(frame.shape[1])

height=int(frame.shape[0])

p = figure(x_range=(0,width), y_range=(0,height), output_backend="webgl", width=width, height=height)

myImage = p.image_rgba(image=[frame], x=0, y=0, dw=width, dh=height)

show(p, notebook_handle=True)

while True:

    ret2, frame = cap2.read()

    if not ret2:

        break

    frame=cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)

    frame=cv2.flip(frame, -1)

    myImage.data_source.data['image']=[frame]

    push_notebook()

    time.sleep(0.01)

cap2.release()